SoroushVahidi/scidocs huggingface dataset:
On a Methodology for Robust Segmentation of Nonideal Iris Images
(2010)

Iris biometric is one of the most reliable biometrics with respect to performance. However, this reliability is a function of the ideality of the data. One of the most important steps in processing nonideal data is reliable and precise segmentation of the iris pattern from remaining background. In this paper, a segmentation methodology that aims at compensating various nonidealities contained in iris images during segmentation is proposed. The virtue of this methodology lies in its capability to reliably segment nonideal imagery that is simultaneously affected with such factors as specular reflection, blur, lighting variation, occlusion, and off-angle images. We demonstrate the robustness of our segmentation methodology by evaluating ideal and nonideal data sets, namely, the Chinese Academy of Sciences iris data version 3 interval subdirectory, the iris challenge evaluation data, the West Virginia University (WVU) data, and the WVU off-angle data. Furthermore, we compare our performance to that of our implementation of Camus and Wildes's algorithm and Masek's algorithm. We demonstrate considerable improvement in segmentation performance over the formerly mentioned algorithms.
Digital Image Enhancement and Noise Filtering by Use of Local Statistics
(1980)

Computational techniques involving contrast enhancement and noise filtering on two-dimensional image arrays are developed based on their local mean and variance. These algorithms are nonrecursive and do not require the use of any kind of transform. They share the same characteristics in that each pixel is processed independently. Consequently, this approach has an obvious advantage when used in real-time digital image processing applications and where a parallel processor can be used. For both the additive and multiplicative cases, the a priori mean and variance of each pixel is derived from its local mean and variance. Then, the minimum mean-square error estimator in its simplest form is applied to obtain the noise filtering algorithms. For multiplicative noise a statistical optimal linear approximation is made. Experimental results show that such an assumption yields a very effective filtering algorithm. Examples on images containing 256 × 256 pixels are given. Results show that in most cases the techniques developed in this paper are readily adaptable to real-time image processing.
Automatic Signature Verification: The State of the Art - 1989-1993
(1994)

This paper is a follow up to an article published in 1989 by R. Plamondon and G. Lorette on the state of the art in automatic signature verification and writer identification. It summarizes the activity from year 1989 to 1993 in automatic signature verification. For this purpose, we report on the different projects dealing with dynamic, static and neural network approaches. In each section, a brief description of the major investigations is given.
Photo Aesthetics Ranking Network with Attributes and Content Adaptation
(2016)

A Aesthetics & Attribute Database (AADB) Fusing Attributes and Content for Aesthetics Ranking Demo, code and model can be download through project webpage http://www.ics.uci.edu/~skong2/aesthetics.html References: [8] He, K., Zhang, X., Ren, S., Sun, J., ECCV, 2014 [15] Lu, X., Lin, Z., Jin, H., Yang, J., Wang, J., IEEE Trans. on Multimedia, 2015 [16] Lu, X., Lin, Z., Jin, H., Yang, J., Wang, J.Z., ACMMM, 2014 [17] Lu, X., Lin, Z., Shen, X., Mech, R., Wang, J.Z., ICCV, 2015 [23] Murray, N., Marchesotti, L., Perronnin, F., CVPR, 2012 Acknowledgements: This work was supported by Adobe gift fund, NSF grants DBI-1262547 and IIS1253538. Experimental Results We use Spearman's rho rank correlation ( ) to measure ranking performance . By thresholding the rating scores, we achieve state-of-the-art classification accuracy on AVA despite never training with a classification loss. We first train a simple model with Euclidean loss for numerical rating of photo aesthetics (a) fine-tuning with rank loss Based on the regression net, we apply rank loss to fine-tune the network
The Homogeneous Chaos Author ( s ) :
(2007)

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The Wiener-Askey Polynomial Chaos for Stochastic Differential Equations
(2002)

We present a new method for solving stochastic differential equations based on Galerkin projections and extensions of Wiener’s polynomial chaos. Specifically, we represent the stochastic processes with an optimum trial basis from the Askey family of orthogonal polynomials that reduces the dimensionality of the system and leads to exponential convergence of the error. Several continuous and discrete processes are treated, and numerical examples show substantial speed-up compared to Monte Carlo simulations for low dimensional stochastic inputs.
Modeling Multibody Dynamic Systems With Uncertainties . Part I : Theoretical and Computational Aspects
(2004)

This study explores the use of generalized polynomial chaos theory for modeling complex nonlinear multibody dynamic systems in the presence of parametric and external uncertainty. The polynomial chaos framework has been chosen because it offers an efficient computational approach for the large, nonlinear multibody models of engineering systems of interest, where the number of uncertain parameters is relatively small, while the magnitude of uncertainties can be very large (e.g., vehicle-soil interaction). The proposed methodology allows the quantification of uncertainty distributions in both time and frequency domains, and enables the simulations of multibody systems to produce results with “error bars”. The first part of this study presents the theoretical and computational aspects of the polynomial chaos methodology. Both unconstrained and constrained formulations of multibody dynamics are considered. Direct stochastic collocation is proposed as less expensive alternative to the traditional Galerkin approach. It is established that stochastic collocation is equivalent to a stochastic response surface approach. We show that multi-dimensional basis functions are constructed as tensor products of one-dimensional basis functions and discuss the treatment of polynomial and trigonometric nonlinearities. Parametric uncertainties are modeled by finite-support probability densities. Stochastic forcings are discretized using truncated Karhunen-Loeve expansions. The companion paper “Modeling Multibody Dynamic Systems With Uncertainties. Part II: Numerical Applications” illustrates the use of the proposed methodology on a selected set of test problems. The overall conclusion is that despite its limitations, polynomial chaos is a powerful approach for the simulation of multibody systems with uncertainties.
High dimensional polynomial interpolation on sparse grids
(2000)

We study polynomial interpolation on a d-dimensional cube, where d is large. We suggest to use the least solution at sparse grids with the extrema of the Chebyshev polynomials. The polynomial exactness of this method is almost optimal. Our error bounds show that the method is universal, i.e., almost optimal for many different function spaces. We report on numerical experiments for d = 10 using up to 652 065 interpolation points.
A human parietal face area contains aligned head-centered visual and tactile maps
(2006)

Visually guided eating, biting and kissing, and avoiding objects moving toward the face and toward which the face moves require prompt, coordinated processing of spatial visual and somatosensory information in order to protect the face and the brain. Single-cell recordings in parietal cortex have identified multisensory neurons with spatially restricted, aligned visual and somatosensory receptive fields, but so far, there has been no evidence for a topographic map in this area. Here we mapped the organization of a multisensory parietal face area in humans by acquiring functional magnetic resonance images while varying the polar angle of facial air puffs and close-up visual stimuli. We found aligned maps of tactile and near-face visual stimuli at the highest level of human association cortex—namely, in the superior part of the postcentral sulcus. We show that this area may code the location of visual stimuli with respect to the face, not with respect to the retina.*NOTE: In the version of this article initially published online, there was an error in the affiliation in the html version. The first affiliation should read Department of Cognitive Science, University of California San Diego, La Jolla, California 92093, USA. The error has been corrected online.
Deconstructing Dynamic Symbolic Execution
(2015)

Dynamic symbolic execution (DSE) is a well-known technique for automatically generating tests to achieve higher levels of coverage in a program. Two keys ideas of DSE are to: (1) seed symbolic execution by executing a program on an initial input; (2) using concrete values from the program execution in place of symbolic expressions whenever symbolic reasoning is hard or not desired. We describe DSE for a simple core language and then present a minimalist implementation of DSE for Python (in Python) that follows this basic recipe. The code is available at https://www.github.com/thomasjball/PyExZ3/ (tagged “v1.0”) and has been designed to make it easy to experiment with and extend.
Electronic health records implementation: An evaluation of information system impact and contingency factors
(2014)

OBJECTIVE
This paper provides a review of EHR (electronic health record) implementations around the world and reports on findings including benefits and issues associated with EHR implementation.


MATERIALS AND METHODS
A systematic literature review was conducted from peer-reviewed scholarly journal publications from the last 10 years (2001-2011). The search was conducted using various publication collections including: Scopus, Embase, Informit, Medline, Proquest Health and Medical Complete. This paper reports on our analysis of previous empirical studies of EHR implementations. We analysed data based on an extension of DeLone and McLean's information system (IS) evaluation framework. The extended framework integrates DeLone and McLean's dimensions, including information quality, system quality, service quality, intention of use and usage, user satisfaction and net benefits, together with contingent dimensions, including systems development, implementation attributes and organisational aspects, as identified by Van der Meijden and colleagues.


RESULTS
A mix of evidence-based positive and negative impacts of EHR was found across different evaluation dimensions. In addition, a number of contingent factors were found to contribute to successful implementation of EHR.


LIMITATIONS
This review does not include white papers or industry surveys, non-English papers, or those published outside the review time period.


CONCLUSION
This review confirms the potential of this technology to aid patient care and clinical documentation; for example, in improved documentation quality, increased administration efficiency, as well as better quality, safety and coordination of care. Common negative impacts include changes to workflow and work disruption. Mixed observations were found on EHR quality, adoption and satisfaction. The review warns future implementers of EHR to carefully undertake the technology implementation exercise. The review also informs healthcare providers of contingent factors that potentially affect EHR development and implementation in an organisational setting. Our findings suggest a lack of socio-technical connectives between the clinician, the patient and the technology in developing and implementing EHR and future developments in patient-accessible EHR. In addition, a synthesis of DeLone and McLean's framework and Van der Meijden and colleagues' contingent factors has been found useful in comprehensively understanding and evaluating EHR implementations.
Probabilistic Models for Expert Finding
(2007)

A common task in many applications is to find persons who are knowledgeable about a given topic (i.e., expert finding). In this paper, we propose and develop a general probabilistic framework for studying expert finding problem and derive two families of generative models (candidate generation models and topic generation models) from the framework. These models subsume most existing language models proposed for expert finding. We further propose several techniques to improve the estimation of the proposed models, including incorporating topic expansion, using a mixture model to model candidate mentions in the supporting documents, and defining an email count-based prior in the topic generation model. Our experiments show that the proposed estimation strategies are all effective to improve retrieval accuracy.
Improving Learning and Inference in a Large Knowledge-Base using Latent Syntactic Cues
(2013)

Automatically constructed Knowledge Bases (KBs) are often incomplete and there is a genuine need to improve their coverage. Path Ranking Algorithm (PRA) is a recently proposed method which aims to improve KB coverage by performing inference directly over the KB graph. For the first time, we demonstrate that addition of edges labeled with latent features mined from a large dependency parsed corpus of 500 million Web documents can significantly outperform previous PRAbased approaches on the KB inference task. We present extensive experimental results validating this finding. The resources presented in this paper are publicly available.
Semantic Framework for Comparison Structures in Natural Language
(2015)

Comparison is one of the most important phenomena in language for expressing objective and subjective facts about various entities. Systems that can understand and reason over comparative structure can play a major role in the applications which require deeper understanding of language. In this paper we present a novel semantic framework for representing the meaning of comparative structures in natural language, which models comparisons as predicate-argument pairs interconnected with semantic roles. Our framework supports not only adjectival, but also adverbial, nominal, and verbal comparatives. With this paper, we provide a novel dataset of gold-standard comparison structures annotated according to our semantic framework.
Cross-Entropy Clustering
(2014)

We build a general and highly applicable clustering theory, which we call cross-entropy clustering (shortly CEC) which joins advantages of classical kmeans (easy implementation and speed) with those of EM (affine invariance and ability to adapt to clusters of desired shapes). Moreover, contrary to k-means and EM, CEC finds the optimal number of clusters by automatically removing groups which carry no information. Although CEC, similarly like EM, can be build on an arbitrary family of densities, in the most important case of Gaussian CEC the division into clusters is affine invariant, while the numerical complexity is comparable to that of k-means.
Model-based vision: a program to see a walking person
(1983)

For a machine to be able to ‘see’, it must know something about the object it is ‘looking’ at, A common method in machine vision is to provide the machine with general rather than specific knowledge about the object. An alternative technique, and the one used in thti paper, b a model-based approach in which particulars aboat the object are given and this drives the analysis. The computer program described here, the WALKER model, maps images into a description in which a person is represented by the series of hierarchical levels, i.e. a person has an arm which has a lower-arm which has a hand. l7te performance of the program ti illustrated by superimposing the machinegenerated picture over the original photographic images.
What is Coordination Theory and How Can It Help Design Cooperative Work Systems?
(1990)

It is possible to design cooperative work tools based only on “common sense” and good intuitions. But the history of technology is replete with examples of good theories greatly aiding the development of useful technology. Where, then, might we look for theories to help us design computer-supported cooperative work tools? In this paper, we will describe one possible perspective—the interdisciplinary study of coordination—that focuses, in part, on how people work together now and how they might do so differently with new information technologies.
In one sense, there is little that is new about the study of coordination. Many different disciplines—including computer science, sociology, political science, management science, systems theory, economics, linguistics, and psychology—have all dealt, in one way or another, with fundamental questions about coordination. Furthermore, several previous writers have suggested that theories about coordination are likely to be important for designing cooperative work tools (e.g., [Holt88], [Wino86]).
We hope to suggest here, however, that the potential for fruitful interdisciplinary connections concerning coordination is much greater than has as yet been widely appreciated. For instance, we believe that fundamentally similar coordination phenomena arise—unrecognized as such—in many of the fields listed above. Though a single coherent body of theory about coordination does not yet exist, many different disciplines could both contribute to and benefit from more general theories of coordination. Of particular interest to researchers in the field of computer-supported cooperative work is the prospect of drawing on a much richer body of existing and future work in these fields than has previously been suggested.
In this paper, we will first describe what we mean by “coordination theory” and give examples of how previous research on computer-supported cooperative work can be interpreted from this perspective. We will then suggest one way of developing this perspective further by proposing tentative definitions of coordination and analyzing its components in more detail.
The More You Know: Using Knowledge Graphs for Image Classification
(2017)

One characteristic that sets humans apart from modern learning-based computer vision algorithms is the ability to acquire knowledge about the world and use that knowledge to reason about the visual world. Humans can learn about the characteristics of objects and the relationships that occur between them to learn a large variety of visual concepts, often with few examples. This paper investigates the use of structured prior knowledge in the form of knowledge graphs and shows that using this knowledge improves performance on image classification. We build on recent work on end-to-end learning on graphs, introducing the Graph Search Neural Network as a way of efficiently incorporating large knowledge graphs into a vision classification pipeline. We show in a number of experiments that our method outperforms standard neural network baselines for multi-label classification.
Cryolipolysis for Targeted Fat Reduction and Improved Appearance of the Enlarged Male Breast.
(2015)

BACKGROUND
Pseudogynecomastia refers to benign male breast enlargement due to excess subareolar fat. Standard treatment is surgical excision under general anesthesia, liposuction, or a combination of both.


OBJECTIVE
The safety and efficacy of cryolipolysis was investigated for nonsurgical treatment of pseudogynecomastia.


METHODS AND MATERIALS
Enrollment consisted of 21 males with pseudogynecomastia. Subjects received a first treatment consisting of a 60-minute cryolipolysis cycle, followed by a two-minute massage, and a second 60-minute cycle with 50% treatment area overlap. At 60 days of follow-up, subjects received a second 60-minute treatment. Safety was evaluated by monitoring side effects and adverse events. Efficacy was assessed by ultrasound, clinical photographs, and subject surveys.


RESULTS
Surveys revealed that 95% of subjects reported improved visual appearance and 89% reported reduced embarrassment associated with pseudogynecomastia. Ultrasound showed mean fat layer reduction of 1.6 ± 1.2 mm. Blinded reviewers correctly identified 82% of baseline photographs. Side effects included mild discomfort during treatment and transient paresthesia and tenderness. One case of paradoxical hyperplasia (PH) occurred but likelihood of PH in the male breast is not believed to be greater than in any other treatment area.


CONCLUSION
This study demonstrated feasibility of cryolipolysis for safe, effective, and well-tolerated nonsurgical treatment of pseudogynecomastia.
Variational Inference for Uncertainty on the Inputs of Gaussian Process Models
(2014)

The Gaussian process latent variable model (GP-LVM) provides a flexible approach for non-linear dimensionality reduction that has been widely applied. However, the current approach for training GP-LVMs is based on maximum likelihood, where the latent projection variables are maximized over rather than integrated out. In this paper we present a Bayesian method for training GP-LVMs by introducing a non-standard variational inference framework that allows to approximately integrate out the latent variables and subsequently train a GP-LVM by maximizing an analytic lower bound on the exact marginal likelihood. We apply this method for learning a GP-LVM from iid observations and for learning non-linear dynamical systems where the observations are temporally correlated. We show that a benefit of the variational Bayesian procedure is its robustness to overfitting and its ability to automatically select the dimensionality of the nonlinear latent space. The resulting framework is generic, flexible and easy to extend for other purposes, such as Gaussian process regression with uncertain inputs and semi-supervised Gaussian processes. We demonstrate our method on synthetic data and standard machine learning benchmarks, as well as challenging real world datasets, including high resolution video data.